Integrating Health Technologies in Health Services for Syrian Refugees in Lebanon: Qualitative Study.

BACKGROUND: Lebanon currently hosts around one million Syrian refugees. There has been an increasing interest in integrating eHealth and mHealth technologies into the provision of primary health care to refugees and Lebanese citizens. OBJECTIVE: We aimed to gain a deeper understanding of the potential for technology integration in primary health care provision in the context of the protracted Syrian refugee crisis in Lebanon. METHODS: A total of 17 face-to-face semistructured interviews were conducted with key informants (n=8) and health care providers (n=9) involved in the provision of health care to the Syrian refugee population in Lebanon. Interviews were audio recorded and directly translated and transcribed from Arabic to English. Thematic analysis was conducted. RESULTS: Study participants indicated that varying resources, primarily time and the availability of technologies at primary health care centers, were the main challenges for integrating technologies for the provision of health care services for refugees. This challenge is compounded by refugees being viewed by participants as a mobile population thus making primary health care centers less willing to invest in refugee health technologies. Lastly, participant views regarding the health and technology literacies of refugees varied and that was considered to be a challenge that needs to be addressed for the successful integration of refugee health technologies. CONCLUSIONS: Our findings indicate that in the context of integrating technology into the provision of health care for refugees in a low or middle income country such as Lebanon, some barriers for technology integration related to the availability of resources are similar to those found elsewhere. However, we identified participant views of refugees' health and technology literacies to be a challenge specific to the context of this refugee crisis. These challenges need to be addressed when considering refugee health technologies. This could be done by increasing the visibility of refugee capabilities and configuring refugee health technologies so that they may create spaces in which refugees are empowered within the health care system and can work toward debunking the views discovered in this study.

A network epidemic model for online community commissioning data.

A statistical model assuming a preferential attachment network, which is generated by adding nodes sequentially according to a few simple rules, usually describes real-life networks better than a model assuming, for example, a Bernoulli random graph, in which any two nodes have the same probability of being connected, does. Therefore, to study the propagation of "infection" across a social network, we propose a network epidemic model by combining a stochastic epidemic model and a preferential attachment model. A simulation study based on the subsequent Markov Chain Monte Carlo algorithm reveals an identifiability issue with the model parameters. Finally, the network epidemic model is applied to a set of online commissioning data.

Factors important for women who breastfeed in public: a content analysis of review data from FeedFinder.

OBJECTIVE: To examine how the breastfeeding experience is represented by users of FeedFinder (a mobile phone application for finding, reviewing and sharing places to breastfeed in public). DESIGN: Content analysis using FeedFinder database. SETTING: FeedFinder, UK, September 2013-June 2015. METHODS: Reviews obtained through FeedFinder over a period of 21 months were systematically coded using a conventional content analysis approach, average review scores were calculated for the rating criteria in FeedFinder (comfort, hygiene, privacy, baby facilities) and review texts were analysed for sentiment. We used data from Foursquare to describe the type of venues visited and cross-referenced the location of venues with the Indices of Multiple Deprivation. RESULTS: A total of 1757 reviews were analysed. Of all the reviews obtained, 80% of those were classified as positive, 15.4% were classified as neutral and 4.3% were classified as negative. Important factors that were discussed by women include facilities, service, level of privacy available and qualities of a venue. The majority of venues were classified as cafes (26.4%), shops (24.4%) and pubs (13.4%). Data on IMD were available for 1229 venues mapped within FeedFinder, 23% were located within the most deprived quintile and 16% were located in the least deprived quintile. CONCLUSIONS: Women create content that is positive and informative when describing their breastfeeding experience in public. Public health bodies and business owners have the potential to use the data from FeedFinder to impact on service provision. Further work is needed to explore the demographic differences that may help to tailor public health interventions aimed at increasing breastfeeding rates in the UK.